Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States

Salastekar, Ninad; Maxfield, Charles M.; Hanna, Tarek N.; Krupiński, Elizabeth A.; Heitkamp, Darel E.; Grimm, Lars J.

doi:10.1016/j.acra.2023.01.005

Cited by 17 publications

(14 citation statements)

References 21 publications

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“… 2 In other words, they should position themselves in the driver’s seat of this new technology. This is in agreement with a recent survey 85 of radiology residents in the USA, which agreed or strongly agreed (83%) that AI/ML education should be a part of radiology residency curriculum. Interesting, the area of AI that the residents were most interested in was in acquiring knowledge to troubleshoot an AI tool in the clinical practice, that is, to determine if the AI algorithm was working as it should (82%).…”

Section: Barriers To Adoption Of Aisupporting

confidence: 91%

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Clinical applications of artificial intelligence in radiology

Mello‐Thoms

Mello

2023

The British Journal of Radiology

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The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI’s adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists’ liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.

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Section: Barriers To Adoption Of Aisupporting

confidence: 91%

“…Interesting, the area of AI that the residents were most interested in was in acquiring knowledge to troubleshoot an AI tool in the clinical practice, that is, to determine if the AI algorithm was working as it should (82%). 85 …”

Section: Barriers To Adoption Of Aimentioning

confidence: 99%

Clinical applications of artificial intelligence in radiology

Mello‐Thoms

Mello

2023

The British Journal of Radiology

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“…Recently, Salastekar et al [ 15 ] highlighted the need for education in AI based on a survey among 759 residents in the USA. They found that a majority of radiology residents believed that education in AI should be included in the radiology training program.…”

Section: Discussionmentioning

confidence: 99%

A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist

van Kooten,

Tan,

Hofmeijer

et al. 2024

Insights Imaging

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Objectives To present a framework to develop and implement a fast-track artificial intelligence (AI) curriculum into an existing radiology residency program, with the potential to prepare a new generation of AI conscious radiologists. Methods The AI-curriculum framework comprises five sequential steps: (1) forming a team of AI experts, (2) assessing the residents’ knowledge level and needs, (3) defining learning objectives, (4) matching these objectives with effective teaching strategies, and finally (5) implementing and evaluating the pilot. Following these steps, a multidisciplinary team of AI engineers, radiologists, and radiology residents designed a 3-day program, including didactic lectures, hands-on laboratory sessions, and group discussions with experts to enhance AI understanding. Pre- and post-curriculum surveys were conducted to assess participants’ expectations and progress and were analyzed using a Wilcoxon rank-sum test. Results There was 100% response rate to the pre- and post-curriculum survey (17 and 12 respondents, respectively). Participants’ confidence in their knowledge and understanding of AI in radiology significantly increased after completing the program (pre-curriculum means 3.25 ± 1.48 (SD), post-curriculum means 6.5 ± 0.90 (SD), p-value = 0.002). A total of 75% confirmed that the course addressed topics that were applicable to their work in radiology. Lectures on the fundamentals of AI and group discussions with experts were deemed most useful. Conclusion Designing an AI curriculum for radiology residents and implementing it into a radiology residency program is feasible using the framework presented. The 3-day AI curriculum effectively increased participants’ perception of knowledge and skills about AI in radiology and can serve as a starting point for further customization. Critical relevance statement The framework provides guidance for developing and implementing an AI curriculum in radiology residency programs, educating residents on the application of AI in radiology and ultimately contributing to future high-quality, safe, and effective patient care. Key points • AI education is necessary to prepare a new generation of AI-conscious radiologists. • The AI curriculum increased participants’ perception of AI knowledge and skills in radiology. • This five-step framework can assist integrating AI education into radiology residency programs. Graphical Abstract

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“…Given that the existing literature on the barriers and enablers associated with implementing AI in radiology practice is scant, it is perhaps unsurprising that many radiologists and medical specialists are not aware of these factors, nor how to become more involved in the development and implementation of AI. A study conducted by Ninad et al highlighted this issue, revealing that over half of the radiology residents surveyed (52%, or 109 residents) expressed interest in AI/ML research but lacked guidance or resources to pursue it [ 34 ]. Furthermore, a significant majority of these residents (83%, or 173 residents) agreed that AI/ML should be included in the radiology residency curriculum [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

“…A study conducted by Ninad et al highlighted this issue, revealing that over half of the radiology residents surveyed (52%, or 109 residents) expressed interest in AI/ML research but lacked guidance or resources to pursue it [ 34 ]. Furthermore, a significant majority of these residents (83%, or 173 residents) agreed that AI/ML should be included in the radiology residency curriculum [ 34 ]. These findings show the need for education not only in medical schools but also within radiology residency programs to equip future radiologists with a comprehensive understanding of the potential issues associated with AI implementation.…”

Section: Discussionmentioning

confidence: 99%

Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review

Eltawil,

Atalla,

Boulos

et al. 2023

Tomography

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Objectives: This scoping review was conducted to determine the barriers and enablers associated with the acceptance of artificial intelligence/machine learning (AI/ML)-enabled innovations into radiology practice from a physician’s perspective. Methods: A systematic search was performed using Ovid Medline and Embase. Keywords were used to generate refined queries with the inclusion of computer-aided diagnosis, artificial intelligence, and barriers and enablers. Three reviewers assessed the articles, with a fourth reviewer used for disagreements. The risk of bias was mitigated by including both quantitative and qualitative studies. Results: An electronic search from January 2000 to 2023 identified 513 studies. Twelve articles were found to fulfill the inclusion criteria: qualitative studies (n = 4), survey studies (n = 7), and randomized controlled trials (RCT) (n = 1). Among the most common barriers to AI implementation into radiology practice were radiologists’ lack of acceptance and trust in AI innovations; a lack of awareness, knowledge, and familiarity with the technology; and perceived threat to the professional autonomy of radiologists. The most important identified AI implementation enablers were high expectations of AI’s potential added value; the potential to decrease errors in diagnosis; the potential to increase efficiency when reaching a diagnosis; and the potential to improve the quality of patient care. Conclusions: This scoping review found that few studies have been designed specifically to identify barriers and enablers to the acceptance of AI in radiology practice. The majority of studies have assessed the perception of AI replacing radiologists, rather than other barriers or enablers in the adoption of AI. To comprehensively evaluate the potential advantages and disadvantages of integrating AI innovations into radiology practice, gathering more robust research evidence on stakeholder perspectives and attitudes is essential.

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Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States

Cited by 17 publications

References 21 publications

Clinical applications of artificial intelligence in radiology

Clinical applications of artificial intelligence in radiology

A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist

Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review

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